3643 results about "Timestamp" patented technology

The invention discloses an insurance claim settlement-oriented multi-modal image video evidence analysis method and system. The method comprises the following steps of: acquiring video / image and multi-source data such as metadata, audio, IMU (Inertial Measurement Unit), GPS (Global Positioning System), OBD (On-Board Diagnostic) and the like; calculating content Hash of the video and the audio according to frames, connecting the content Hash with time information in series to form chained Hash, and adding a verification digital signature and a credible timestamp; realizing cross-modal time sequence alignment based on self-adaptive time anchor-attitude coupling; tampering detection is carried out in combination with PRNU fingerprints, noise field consistency, dual compression, copy-movement and the like; multi-view geometry and monocular depth are fused, IMU scale constraint and micro rendering are introduced, three-dimensional reconstruction and re-projection optimization are completed, and collision dynamics verification is carried out; and constructing an event cause and effect graph, judging responsibility in combination with traffic rules, outputting a confidence coefficient vector and a structured report, and generating a verifiable evidence packet. The scheme has the advantages of high efficiency and traceability in the aspects of space-time restoration and interpretable responsibility judgment.

The invention relates to a network attack tracing method and device based on a threat graph, equipment and a medium. The method comprises the steps of obtaining network security log data from multiple security data sources, and performing standardization processing to obtain a structured network security event data set; extracting threat entities and behavior relationships among the threat entities from the structured network security event data set to obtain an entity set and a relationship set; constructing a threat map according to the entity set and the relationship set, and performing time data slicing according to the timestamp to obtain a map snapshot and map metadata; based on a predefined attack chain template, identifying an attack chain sub-graph conforming to an attack behavior structure in the threat graph to obtain an attack chain set and a path reachability matrix; and according to the attack chain set and the path reachability matrix, attack path inversion is carried out by taking the target node as an end point, and an attack traceability path and a graph evolution process display result are obtained. By adopting the method, the network attack path can be identified and the attack source can be traced.

The invention discloses a long video multi-modal understanding and question-answering method and system based on large model and retrieval enhancement generation. The method comprises the following steps: 1) a multi-modal feature extraction module; 2) a multi-modal synchronization and alignment mechanism; 3) constructing a structured memory pool; 4) querying a drive generation mechanism; 5) incremental updating and memory compression strategy; and 6) unifying the multi-modal representation space. The invention provides a long video multi-mode understanding method fusing a large language model and retrieval enhancement generation, and aims to break through the limitation of a traditional method in the aspects of single-mode processing and semantic fragmentation. According to the method, video image features are extracted through a visual model (such as YOLO and ViT), voice transcription and environment voice description are obtained in combination with an audio model (such as Whisper and Qwen-Audio), and unified coding of vision, voice and audio in a long video is achieved. Then, a structured memory pool is constructed through semantic consistency segmentation and timestamp alignment technologies to store time slice data of different modalities.

The invention discloses a multi-modal information fusion odometer construction method for star catalogue positioning, and relates to the technical field of star catalogue patroller positioning. The method comprises the following steps: carrying out space joint calibration on a monocular camera, a laser radar and an inertial measurement unit, reconstructing a laser radar point cloud by using a timestamp of a camera image, and realizing time synchronization of the camera image and the laser radar point cloud; establishing an IMU pre-integration error model; and performing motion compensation distortion removal on the laser point cloud by using an IMU pre-integration result, and extracting geometric features of the distorted laser point cloud based on a neighbor region smoothness calculation method of a fixed measurement distance. By researching a multimodal information fusion odometer method, the accumulative error of motion measurement is reduced, the positioning precision and stability are improved, technical support is provided for design and development of a star catalogue navigation system, and the problems that a single-modal star catalogue positioning method is weak in environment adaptive capacity and poor in algorithm generalization are solved.

The invention discloses a precision compensation method of a composite numerical control machine tool, and particularly relates to the technical field of precision control of the numerical control machine tool, which comprises the following steps: synchronously acquiring temperature, vibration and force information under the driving of a unified clock through multiple types of sensors deployed at key nodes of the machine tool, and generating a synchronous multi-source sensing data set with aligned timestamps; inputting the data set into a multivariable coupling error model obtained through data driving training, and calculating and generating a comprehensive space volume error predicted value of a tool nose point of the tool; performing inverse calculation based on a machine tool kinematic chain model, and generating a multi-dimensional micro-compensation instruction set containing the compensation amount of each motion axis and the sequential relationship; and finally, dynamically writing the instruction set into a servo control ring of the numerical control system through a real-time data interface, and driving each motion shaft to execute synchronous compensation motion. According to the method, comprehensive compensation of multi-physics field coupling errors such as temperature, vibration and force load is achieved, and the machining precision and stability of the numerical control machine tool under complex working conditions are improved.

The invention relates to the technical field of heat management of energy storage systems, and discloses a heat management method of an intelligent distributed liquid cooling energy storage system. The method comprises the following steps: acquiring operation state parameters of a plurality of single batteries and optimizing timestamp synchronization; and processing the temperature data by adopting small-batch time series decomposition, and extracting change trend characteristics and abnormal hot spot positions. And based on the information, optimizing a heat exchange control rule base parameter set through a non-dominated sorting strategy, and generating optimized control parameters and priority rules. And in combination with inlet temperature and flow parameters of the liquid cooling system, a tabu search mechanism is utilized to decide a cooling liquid flow distribution scheme, and a branch control instruction is output. And finally, a flow constraint boundary is dynamically adjusted by adopting an approximate gradient projection technology, and a branch and bound strategy is fused to discretize and optimize a distribution path, so that the branch flow is finely regulated and controlled, and the thermal management precision and reliability of the energy storage system are improved. According to the method, the accuracy, adaptability and stability of thermal management of the liquid cooling energy storage system are improved.

The invention discloses an explosion-proof equipment full life cycle digital management system based on artificial intelligence, and relates to the technical field of equipment management. The working process of the system comprises the following steps: integrating equipment attributes, operation and maintenance records and environment variable data, calculating a performance attenuation value through a weighting formula, and standardizing the data; correcting an abnormal timestamp by adopting a dynamic time window, realizing cross-system equipment identity mapping in combination with Hash similarity and parameter matching degree, and reconstructing a three-dimensional feature tensor; equipment is divided into three types, and differential weighted pooling processing is executed to generate a classification feature matrix; a reference parameter curve is generated through exponential decay weighting, a normalized deviation score of the fusion environment factors is calculated, and a grading early warning mechanism is triggered; generating an early warning report; and implementing a closed-loop strategy according to the early warning level. The system solves the problems of equipment identity confusion, environment-parameter coupling quantification and the like, realizes full-chain intelligent management from data acquisition to risk disposal, and improves the safety and operation and maintenance efficiency of explosion-proof equipment.

The invention discloses a computer network data secure transmission system and method, and particularly relates to the technical field of network data secure transmission, and the system comprises a dynamic key management module which generates a key seed through a quantum random number generator, generates a dynamic key bound with a data packet in combination with a timestamp, and transmits the dynamic key to a server; after being encrypted by a receiving end public key, the data are transmitted through an independent verification channel; the dual-path transmission control module is used for establishing dual channels of a main path and a shadow path, a data packet of the main path is embedded into a random camouflage protocol header to simulate a non-sensitive protocol, and a blank data packet is filled in the shadow path to maintain traffic characteristics; according to the real-time verification engine, a receiving end constructs an encrypted hash tree to achieve fragment-level integrity verification, and meanwhile, requests key state three-state verification from the key management module. Through key dynamic generation and separation transmission, dual-path adaptive fragmentation distribution, fragmentation hash tree reconstruction and key linkage verification, and abnormal triggering fragmentation level retransmission, the problems of long-term effectiveness of a static key, predictable transmission path and verification lag are solved.

The invention, which relates to the technical field of intelligent interaction, discloses an AI-based adaptive scene intelligent interaction system comprising a multi-modal data acquisition module, a modal preprocessing module, a multi-modal embedded coding module, an intention fusion and representation module, a service scene matching module and a service execution and reinforcement learning module. The method comprises the following steps: acquiring multi-modal original data in a user interaction process, including voice signals, text input and user behavior tracks, and synchronously recording an acquisition timestamp; according to the method, through a multi-modal unified embedding and dynamic weighting mechanism, the problem of characteristic dimension imbalance is effectively solved, and the user intention recognition accuracy is improved; meanwhile, reinforcement learning and a multi-factor scoring model are combined, personalized scene matching and dynamic response are achieved, the adaptive capacity and service accuracy of the system in a complex environment are improved, and therefore the stability and user experience of the intelligent interaction system are remarkably optimized.

The invention discloses a mine disaster prediction method based on multi-source data, and relates to the technical field of mine safety, and the method comprises the following steps: collecting original data from different types of sensors in a mine, the data types comprising gas concentration, temperature, humidity, wind speed, ground pressure, water level and vibration information; each type of data is provided with a corresponding timestamp and a spatial position identifier. According to the method, time resampling and space mapping standardization of multi-source data are realized, so that the time-space consistency of data fusion is remarkably improved, and the accuracy of disaster prediction model input is ensured. Meanwhile, a dynamic feature matrix is constructed and a high-precision position weight mechanism is introduced, so that the sensitivity of the model to key areas and key parameters is enhanced, the real-time performance and accuracy of mine disaster prediction are effectively improved, and the risk of missing report and false report of an early warning system is remarkably reduced.

The invention discloses a short video intelligent editing method and system based on multi-modal analysis, and relates to the technical field of video editing. The method is used for improving editing efficiency and visual experience and comprises the following steps: extracting lip motion features of a character, visual saliency features of a commodity and a voice emotion intensity value from a target short video stream to form multi-modal time sequence data; afterwards, the voice stream is recorded, a product keyword timestamp is extracted, the alignment degree is calculated through dynamic time warping in combination with a visual saliency peak value, and a preliminary editing point set is generated through weighted evaluation in combination with an emotional intensity value; constructing an editing decision optimization model based on deep reinforcement learning, taking the multi-modal features as state input, adjusting the retention probability of editing points through a joint reward function, and selecting an optimal transition mode; and the lip movement and voice synchronization error before and after the editing point and the emotional and visual continuity of the transition section are analyzed, the discontinuous region is smoothed, and the edited finished product is output, so that precise short video intelligent editing is realized.

The invention discloses a foreign trade e-commerce cloud network aggregation supervision method based on cloud service, and the method comprises the following steps: S1, collecting cross-border e-commerce transaction data, logistics distribution data, storage data and customs supervision data in real time, and carrying out the standardization processing through a distributed cleaning method; s2, constructing a multi-source heterogeneous graph according to the standardized data; s3, marking types, relation types and timestamps for nodes and edges in the graph, and extracting high-dimensional features through a relation enhancement graph Transform; s4, performing local and global aggregation through a multi-layer attention mechanism to generate an aggregation view; s5, constructing a multi-target supervision task model, and generating a resource scheduling scheme; s6, transaction, logistics and supervision abnormity is identified, and a risk early warning and intervention strategy is generated; and S7, dynamically optimizing the relation structure and Transform parameters according to the execution feedback. According to the method, fusion supervision and risk early warning of foreign trade e-commerce cross-domain multi-source data are realized based on high-dimensional graph expression and an optimization scheduling mechanism.

A predictive incident management system, consisting of: a sensor input module configured to receive heterogeneous telemetry data streams from mechanical, thermal, electrical and cyber sources; a temporal correlation control unit operationally coupled to the sensor input module, wherein the temporal correlation control unit is configured to normalize received data into a uniform time series envelope that includes identifiers, microsecond-precision timestamps, metric names, values, and context markers, and is further configured to compute sliding window-cross-sensor correlation matrices, event motifs, and lead-lag dependencies across multiple time granularities; a scalable rule processor that is communicatively linked to the control unit for temporal correlation, wherein the rule engine includes an in-memory runtime environment for processing complex events and a domain-specific declarative language, and is configured to apply rules that reference primitive sensor metrics, derived correlation features, and motive-based early warning vectors to classify, escalate, or resolve predicted incidents; A historical repository that is communicatively connected to both the temporal correlation control unit and the rule engine. The repository is configured to store tagged event histories, correlation motif dictionaries, rule versions, and rule origin metadata to ensure the verifiability and explainability of predictions; and An incident response interface is operationally connected to the rule engine. The incident response interface is configured to trigger automated workflows, including the generation of tickets for IT service management, chat ops notifications, the execution of orchestration playbooks, and direct machine control via industrial protocols. the system is configured to perform predictive analyses based on temporal correlations between sensors and to execute context-aware, rule-based incident management in real time.

The invention discloses a container application on-screen method based on texture full-process optimization, which comprises the following steps: taking a swan-gap system as a host and an Android system as a container, distributing a plurality of first buffers by the host, screening out a first texture format by the container, writing original textures of the format generated by rendering into frame buffers, identifying a dirty area of the rendered textures, and displaying the original textures in the frame buffers; packaging the texture data of the region, storing the packaged texture data into a shared memory, transmitting a file descriptor FD to a host, binding an idle first buffer setting priority, a state, a generation timestamp and a texture data pointer at the same time, and obtaining the texture data by the host through the FD; the host establishes a fixed thread task pool, executes multiple tasks in parallel to obtain on-screen texture data, and then updates the state of the first buffer and a texture data pointer; and finally, the host queries the first buffer with the processed state, reads the texture data after sorting according to the priorities and the generation timestamps to finish on-screen, and resets the state of the first buffer to be idle, so that the on-screen delay is effectively reduced, and the actual frame rate of the Android application is improved.

The invention discloses an intelligent operation and maintenance method for modular data acquisition and analysis sewage treatment equipment, which comprises the following steps: designing a modular data acquisition unit, configuring an acquisition module with a standardized interface, and realizing quick access of multi-source equipment; protocol conversion, data compression and timestamp completion are completed by using an edge computing gateway, and stable data reporting under a complex network is realized by combining an MQTT protocol; a time sequence database is adopted to store and fuse OA / ERP system data, and an information island is broken; remote diagnosis, intelligent early warning and visual decision making are realized by means of a remote fault analysis interface, a user-defined rule engine and a configurable billboard; and operation and maintenance closed-loop management is achieved in cooperation with a workflow engine. The method effectively solves the problems of low data acquisition integration level, unstable communication, weak analysis capability and the like in traditional operation and maintenance, has the characteristics of modularization, intelligence, high efficiency and the like, remarkably improves the operation and maintenance management level of the sewage treatment equipment, reduces the operation and maintenance cost, and has a wide application prospect.

The invention relates to the technical field of product traceability, in particular to a traceability system and method based on a block chain technology, and the system comprises a right confirmation registration module, a parameter verification module, a quality inspection label module, a logistics locking module and a traceability auditing module. According to the method, compliance evaluation is carried out by comparing environmental parameters bound with the operation time period and the land parcel information with an agricultural standard boundary, systematic judgment of land parcel growth conditions can be realized, confidence modeling is carried out on a quality inspection data layer based on a difference value amplitude and a batch mean value, credible label data of batch quality is formed, and the reliability of the quality inspection data layer is improved. Through sequence comparison of storage time periods and carrying timestamps and environmental parameter change frequency deviation judgment, abnormal carrying behaviors and storage fluctuation conditions can be identified, a traceability identification value and quality traceability code bidirectional verification mechanism is introduced in a query response stage, and credible labels, quality inspection states and position information are displayed in an aggregated manner. And the completeness, the accuracy and the result credibility of the traceability query result are obviously improved.

The invention discloses an archive digitization full-life-cycle encryption integrity verification method and system, relates to the field of digital archive safety management, and is used for solving the problem of integrity and safety guarantee in archive digitization management. Logic units are divided through a semantic analysis engine, non-uniform blocks are generated based on sensitivity dynamic partitioning, local hash values are calculated, and a multi-level verification tree is constructed to generate root hash and a hash path mapping table; acquiring visitor identities, terminal environments and timestamps in each stage of an archive life cycle, generating state snapshots in combination with a hash path, and constructing a life cycle state snapshot chain; reconstructing a target logic unit verification path according to the access request, analyzing the structural difference between a current path and a historical snapshot chain through a path reconstruction type difference verification algorithm, and identifying structural offset, node tampering or path loss; and response control is triggered for abnormal verification, including session key updating and permission freezing, so that the security integrity guarantee of the digital archive in the full life cycle is improved.

A system, a method and a processor are for AI-powered generation and delivery of video clips. The processor is configured to: load a first video file of a first video content item, the first video file comprising video frames associated with timestamps; load a first subtitle file of the first video content item, the first subtitle file comprising subtitle text associated with the timestamps; execute a natural language processing (NLP) model with the subtitle text as input, the NLP model including language pre-processing steps for classifying words, names or phrases in the subtitle text and associating initial classifiers with the subtitle text, the NLP model including one or more of a recurrent neural network (RNN), a Bidirectional Encoder Representations from Transformers (BERT) model, or a generative pre-trained transformer (GPT) model for a dialogue analysis comprising processing sequences of dialogue in the subtitle text in view of the initial classifiers to associate one or more portions of the dialogue with one or more first classifiers of first narrative elements; execute an image recognition model with at least some of the video frames as input, the image recognition model including a convolutional neural network (CNN) for an object detection analysis and a facial recognition analysis comprising processing video sequences to associate one or more of the video frames with one or more second classifiers of second narrative elements; generate a narrative map of the first video content item by temporally aligning the first narrative elements with the second narrative elements based on the timestamps associated with the video frames and the first subtitle file; and generate a video clip including at least one segment of the first video content item, the at least one segment including selected video frames associated with at least one of the first or second narrative elements identified from the narrative map and selected for inclusion in the video clip.

The invention discloses a guideboard surface deformation detection method and system based on a dynamic video sequence. The method comprises the following steps: acquiring road video data with a timestamp and position information; a traffic sign is automatically identified from the road video data, and geometric anomaly detection is carried out; a time sequence tracking sequence is established for the guideboard, and whether continuous abnormity exists in the guideboard is determined through multi-frame analysis; if yes, performing local texture consistency and local contour geometric analysis on the guideboard to obtain a fusion analysis result; performing state evaluation on supporting equipment corresponding to the guideboard to obtain a state evaluation result of the guideboard supporting equipment; and determining the final surface deformation state and the corresponding grade of the guideboard based on the fusion analysis result and the state evaluation result of the guideboard supporting equipment. By implementing the method provided by the invention, automatic detection and quantitative analysis of the bending or deformation condition of the road sign can be realized without manual intervention, and early warning information can be output in time.

The invention discloses a live broadcast e-commerce compliance auditing method and system based on penetration type supervision, and relates to the technical field of e-commerce compliance supervision, and the method comprises the steps: carrying out the Hash abstract calculation of a judgment result data set and a multi-source data set, carrying out the partitioning storage of the data set in a block chain, verifying the storage process through a timestamp and a multi-node consensus protocol, and carrying out the verification of the verification result; forming an evidence storage data chain; automatically matching the evidence storage data chain with a compliance rule, identifying potential illegal behaviors and risk levels, and forming a compliance result set; performing fusion calculation on the compliance result set, historical violation records and user feedback data to generate a comprehensive risk score, and automatically triggering real-time compliance intervention measures according to the comprehensive risk score to form an intervention result set; and re-storing the intervention result set, the comprehensive risk score and the violation behavior in the block chain to form a closed-loop evidence storage data chain. According to the invention, supervision transparency is enhanced, data integrity is ensured, and reliable support is provided for real-time intervention and closed-loop auditing.

The invention relates to the technical field of inventory management, in particular to an intelligent warehouse management system for main shaft parts based on digital twinning, which comprises a main shaft mapping module for acquiring a main shaft shipping space and an allocation path, comparing shipping space nodes with a moving timestamp difference to generate an identification result, the fluctuation identification module extracts channel nodes to analyze fluctuation threshold deviation and generate a report, the allocation control module generates a scheduling list based on queuing duration and shipping space data distribution weight, and the abnormity positioning module extracts unallocated parts to analyze interface difference and mark abnormal links. And the state early warning module integrates four indexes of abnormal data identification risk paths to generate a management scheme. According to the method, bin position nodes and main shaft movement timestamps are synchronously compared to identify difference nodes, a dynamic weight distribution mechanism is established in combination with channel node fluctuation analysis, an interruption link difference trend and interface delay data are integrated to construct a risk assessment model, and abnormal conduction path positioning and early warning are realized. And scheduling conflicts and resource waste caused by data deviation are reduced.

The invention discloses an OBD tamper-proof two-way verification method, which comprises the steps of obtaining OBD equipment data and vehicle sensor data, registering to a cloud end based on the OBD equipment data, obtaining registration data, obtaining key data and non-key data according to the vehicle sensor data, symmetrically encrypting the key data by using a session key in the registration data, and verifying the key data and the non-key data. Performing approximate homomorphic encryption on the non-key data, obtaining a double-entropy source dynamic salt value according to the time entropy of the ciphertext and the OBD equipment operation entropy, reversely reconstructing the dynamic salt value by the cloud according to the timestamp and the registration data, verifying the ciphertext structure by using the dynamic salt value and generating a random challenge containing a salt value check code, and sending the random challenge to the cloud; the equipment verification result and the challenge value are signed and then transmitted back, the OBD equipment carries out reverse verification on the salt value according to the signature and the equipment verification result, and the cloud generates an authority token based on the reverse verification result. According to the method, the data authenticity and integrity of the diagnostic equipment are guaranteed through packet encryption, dynamic salt value generation and bidirectional identity verification of transmission data.

The invention relates to an elevator fault real-time monitoring and early warning method, system and terminal, and belongs to the technical field of elevator monitoring. The monitoring and early warning method comprises the steps that multi-source sensing data of an elevator are collected, and timestamp synchronization is conducted on the multi-source sensing data; calculating a comprehensive risk value of the elevator based on the multi-source sensing data after timestamp synchronization; judging whether the comprehensive risk value is greater than a risk threshold value or not; if yes, Granger causal test is operated, an abnormal causal event is searched for from a pre-constructed causal graph, and relevant data corresponding to the abnormal causal event is packaged and sent to the elevator digital twinborn body; fault acceleration simulation is carried out in the digital twinborn body, and an abnormal root is positioned; and a health assessment report of the elevator is generated according to the simulation process data and the abnormal root. The method has the beneficial effects that the actual running state of the elevator is truly reflected, so that the running safety and reliability of the elevator are improved.

An end-to-end rendering system enforces policy-constrained, symbolic presentation of artificial-intelligence (AI) outputs prior to any pixel emission. An AI accelerator emits inference tokens as (token identifier, confidence, domain tag) tuples into an output FIFO. A graphics processor with a command processor and SIMD co-processor executes a policy engine that runs a deterministic finite automaton (DFA) stored in non-transitory memory. The DFA consumes the tuples and produces, before any frame-buffer writes, a permit / deny decision and a visibility mask that constrain presentation attributes. A glyph selector maps permitted tuples to entries in a constrained glyph dictionary specifying a glyph identifier, semantic class, and allowed substitutions. A provenance tagger computes a cryptographic hash over at least the glyph identifier, a DFA-state policy identifier, a session nonce, and a checksum of the tuples to create an evidence capsule bound to the glyph. A device-aware renderer, consulting a device profile registry and a rendering grammar, emits a renderable asset for the glyph only when permitted by the visibility mask and grammar. A justification ledger records, for each displayed glyph, the evidence capsule and a monotonic timestamp. Executed entirely within the graphics processor, the pipeline prevents unauthorized tokens from entering the display path, enables cryptographically verifiable traceability of visible content, and reduces bandwidth by representing outputs as glyph identifiers rather than text or raster imagery, while allowing runtime policy updates without modifying the underlying inference model.

A dynamic real estate valuation system based on multiparametric market indicators, which includes the following: a valuation engine configured to generate real-time results for property valuation; a multitude of distributed data ingestion and processing units configured to capture heterogeneous data sources, including historical property transaction data, real-time property listings, zoning and land use records, macroeconomic indicators, geospatial data, environmental sensor outputs, and sentiment-derived metrics; a model orchestration control unit comprising a stack of machine learning models, wherein the models include at least a gradient boosting decision tree model, a long-short-term memory (LSTM) time series forecaster, and an enhancement learning module that iteratively optimizes the model parameters based on the observed evaluation accuracy; a data contextualization controller configured to apply dynamic weighting to each input parameter based on the geographic, temporal, and market context by executing decay functions and location-specific rule matrices; a physical property valuation terminal (PVT) that includes an edge processing unit (EPU), geolocation circuitry, secure communication interfaces and a touch-based user interface; a valuation book subsystem configured to hash the valuation output, timestamp, and signatures of the input record into a blockchain-based distributed ledger; wherein the system is designed to continuously recalibrate its valuation results by comparing the predicted valuations with the actual sales or rental prices, and wherein the physical terminal is designed to produce a legally certifiable valuation document with embedded provenance data.

The invention relates to the technical field of execution traceability, in particular to a block chain-based flood storage and detention area ecological compensation execution traceability processing method and system, and the method comprises the following steps: extracting remote sensing data to generate a delay factor list, recognizing ecological trigger nodes to generate a calibration node data set, constructing a reconciliation path structure, and extracting boundary nodes; and generating a permission mapping and responsibility signature block, and writing the permission mapping and responsibility signature block into a responsibility tracking chain structure area. According to the method, logic reconciliation is carried out on compensation and payment behaviors through timestamps and hash records, node tracking and continuity inspection of fund payment paths are realized, operation authority matching is carried out in combination with a contract authority table after time jump is identified and boundary nodes are calibrated, and a responsibility tracking structure chain is constructed step by step; according to the method, responsibility binding of fund behavior nodes and whole-process signature traceability and chain traceability of a transfer behavior are effectively realized, the authenticity discrimination capability of compensation execution and the operation responsibility traceability capability are enhanced, and the verification precision and process integrity of a fund execution path are improved.

The invention relates to a food traceability method based on a block chain, and the method comprises the steps: collecting production data, generating a unique data major key and an identifier, encrypting and uploading the data, and binding the data with a food batch; associating production link data, collecting processing data, performing secondary hash to generate a processing link data link, and binding a food unit after the processing link data link is linked; identifying a processing link identifier, collecting transportation data, carrying out chain association and timestamp progressive encryption and then carrying out uplink, generating a transportation link identifier and recording handover information; after full-link data are verified, sales data are collected, a full-link Merkel tree is formed through closed-loop association, a sales link identifier is generated after uplink encryption, and multi-node confirmation is triggered; a permission verification request is initiated through a query terminal, and after block chain type verification and local hash verification are carried out, a complete traceability link containing handover nodes, persons in charge and equipment information is displayed according to a time axis. According to the invention, full-life-cycle traceability of food is realized.

The invention discloses a multi-modal content intelligent auditing and violation detection method and system, and relates to the technical field of information processing. The method comprises the following steps: carrying out audio-picture separation on a video stream, and carrying out parallel processing on audio-to-text and visual key frame extraction; a space-time encoder is constructed to record the corresponding relation of the time stamps of all the modes; constructing a multi-modal resource target dictionary, and forming an inter-entity knowledge graph by using relation categories; calculating a confidence coefficient difference index between modals by comparing, learning and training the shared semantic space; and carrying out violation judgment, triggering a sensitive characteristic threshold value for any mode, and starting multi-mode evidence cross validation. According to the method, audio and picture separation is carried out on the video stream, parallel processing is carried out on audio-to-text and visual key frame extraction, a multi-modal resource target dictionary is constructed, violation judgment is carried out according to confidence coefficient difference indexes among modals, and false information auditing efficiency and detection efficiency are improved.

The invention relates to the technical field of data processing and analysis, and discloses a parallel sensor data analysis method and system for cable fault detection, and the method comprises the steps: synchronously collecting cable fault transient traveling wave signals through distributed monitoring terminals, and forming a multi-channel signal data set; and carrying out wavelet packet decomposition and reconstruction, and denoising to obtain a denoised data set. The method comprises the following steps: firstly, generating node time sequence data with a timestamp, constructing an undirected weighted graph model, calculating a plurality of candidate fault positions through a double-end positioning formula on the basis of corrected time data, and finally, allocating dynamic weights to effective candidate positions according to signal quality and confidence, and generating a fusion positioning result by adopting a weighted fusion algorithm. And physical correction is carried out based on cable laying constraints, and accurate fault position coordinates are output. According to the method, through multi-level data processing and fusion, the fault positioning precision and the system robustness under complex working conditions are improved.

The invention relates to a space-ground integrated Beidou high-precision positioning method applied to urban canyons, and belongs to the technical field of navigation positioning. The method comprises the following steps: deploying a pseudo satellite base station, calibrating coordinates of the base station through carrier phase difference, and monitoring the number of Beidou visible satellites; a master pseudo-satellite is determined, timestamps and ephemeris data are broadcasted to slave pseudo-satellites through Beidou short messages, and the slave pseudo-satellites are combined with bidirectional ranging data to correct a local clock to realize high-precision synchronization; receiving Beidou and pseudo-satellite signals by a user terminal, screening effective observation values through a shielding judgment mechanism, substituting the effective observation values into a non-difference observation equation in combination with pseudo-satellite reference difference data, and resolving a fusion result through an elastic coupling mode; and finally, multi-path interference is identified by using pseudo-satellite double antennas, and historical data is called to construct an error model to correct a fusion result. The space-ground integrated positioning of Beidou and pseudo satellites in an urban canyon scene is realized, the centimeter-level positioning precision is stably output, and the high-precision positioning requirements of urban traffic, surveying and mapping and the like are met.